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Analysis of proportional mean residual life model with latent variables
He, Haijin1; Cai, Jingheng2; Song, Xinyuan3,4; Sun, Liuquan5
2017-02-01
Source PublicationSTATISTICS IN MEDICINE
ISSN0277-6715
Volume36Issue:5Pages:813-826
AbstractEnd-stage renal disease (ESRD) is one of the most serious diabetes complications. Numerous studies have been devoted to revealing the risk factors of the onset time of ESRD. In this article, we propose a proportional mean residual life (MRL) model with latent variables to assess the effects of observed and latent risk factors on the MRL function of ESRD in a cohort of Chinese type 2 diabetic patients. The proposed model generalizes the conventional proportional MRL model to accommodate the latent risk factor that cannot be measured by a single observed variable. We employ a factor analysis model to characterize the latent risk factors via multiple observed variables. We develop a borrow-strength estimation procedure, which incorporates the expectation-maximization algorithm and an extended estimating equation approach. The asymptotic properties of the proposed estimators are established. Simulation shows that the performance of the proposed methodology is satisfactory. The application to the study of type 2 diabetes reveals insights into the prevention of ESRD. Copyright (C) 2016 John Wiley & Sons, Ltd.
Keywordborrow-strength estimation extended estimating equations factor analysis latent variables mean residual life function proportional model
DOI10.1002/sim.7174
Language英语
Funding ProjectResearch Grant Council of the Hong Kong Special Administration Region[14601115] ; Research Grant Council of the Hong Kong Special Administration Region[14305014] ; Chinese University of Hong Kong ; National Natural Science Foundation of China[11471277] ; National Natural Science Foundation of China[11231010] ; National Natural Science Foundation of China[11171330] ; Key Laboratory of RCSDS, CAS[2008DP173182]
WOS Research AreaMathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Research & Experimental Medicine ; Mathematics
WOS SubjectMathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Medicine, Research & Experimental ; Statistics & Probability
WOS IDWOS:000393303200007
PublisherWILEY-BLACKWELL
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/24689
Collection应用数学研究所
Affiliation1.Shenzhen Univ, Coll Math & Comp Sci, Shenzhen, Peoples R China
2.Sun Yat Sen Univ, Dept Stat, Guangzhou, Guangdong, Peoples R China
3.Chinese Univ Hong Kong, Shenzhen Res Inst, Hong Kong, Hong Kong, Peoples R China
4.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Hong Kong, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China
Recommended Citation
GB/T 7714
He, Haijin,Cai, Jingheng,Song, Xinyuan,et al. Analysis of proportional mean residual life model with latent variables[J]. STATISTICS IN MEDICINE,2017,36(5):813-826.
APA He, Haijin,Cai, Jingheng,Song, Xinyuan,&Sun, Liuquan.(2017).Analysis of proportional mean residual life model with latent variables.STATISTICS IN MEDICINE,36(5),813-826.
MLA He, Haijin,et al."Analysis of proportional mean residual life model with latent variables".STATISTICS IN MEDICINE 36.5(2017):813-826.
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